Predicting users' preferences by Fuzzy Rough Set Quarter-Sphere Support Vector Machine

Recommender systems aim to support users in decision-making through the knowledge extracted from historical ratings. However, many of these ratings may be noisy and/or missing, causing degradation in the quality of the recommendations. Considering these issues, this paper presents a new one-class classifier to predict ratings in recommendation systems. The proposed method estimates the shared informative neighbors of each user by a probability fuzzy rough set method. Since the fuzzy rough set theory is sensitive to noisy samples, the quarter-sphere SVM classifier is designed to reduce the impact of noise on the results. The proposed classifier can satisfactorily determine a boundary around the target class while it reduces the acceptance probability of the outliers and non-target class(es). The theoretical interpretations are provided to prove the statistical stability of the proposed method. Also, noise analysis has been carried out. Through extensive experiments on several real-world data sets, it is confirmed that the proposed method outperforms the other six methods in terms of accuracy, recall, precision, and computational time. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a new one-class classifier to predict ratings in recommendation systems and reduces the impact of noise on results by estimating shared informative neighbors of each user using a probability fuzzy rough set method and a quarter-sphere SVM classifier. The proposed method outperforms other six methods in terms of accuracy, recall, precision, and computational time, as confirmed through extensive experiments on real-world data sets.